{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streetcar Delay Prediction - Data Exploration\n",
    "\n",
    "Use dataset covering Toronto Transit Commission (TTC) streetcar delays 2014 - 2020 to predict future delays and come up with recommendations for avoiding delays.\n",
    "\n",
    "Source dataset: https://open.toronto.ca/dataset/ttc-streetcar-delay-data/\n",
    "\n",
    "This notebook contains additional data exploration code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streetcar routes\n",
    "\n",
    "From https://www.ttc.ca/Routes/Streetcars.jsp\n",
    "\n",
    "<table style=\"border: none\" align=\"left\">\n",
    "   </tr>\n",
    "   <tr style=\"border: none\">\n",
    "       <th style=\"border: none\"><img src=\"https://raw.githubusercontent.com/ryanmark1867/streetcarnov3/master/streetcar%20routes.jpg\" width=\"600\" alt=\"Icon\"> </th>\n",
    "   </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streetcar vehicle IDs CLRV/ALRV\n",
    "\n",
    "From https://en.wikipedia.org/wiki/Toronto_streetcar_system_rolling_stock#CLRVs_and_ALRVs\n",
    "\n",
    "<table style=\"border: none\" align=\"left\">\n",
    "   </tr>\n",
    "   <tr style=\"border: none\">\n",
    "       <th style=\"border: none\"><img src=\"https://raw.githubusercontent.com/ryanmark1867/streetcarnov3/master/streetcarCLRV.jpg\" width=\"600\" alt=\"Icon\"> </th>\n",
    "   </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streetcar vehicle IDs Flexity\n",
    "\n",
    "From https://en.wikipedia.org/wiki/Toronto_streetcar_system_rolling_stock#CLRVs_and_ALRVs\n",
    "\n",
    "<table style=\"border: none\" align=\"left\">\n",
    "   </tr>\n",
    "   <tr style=\"border: none\">\n",
    "       <th style=\"border: none\"><img src=\"https://raw.githubusercontent.com/ryanmark1867/streetcarnov3/master/streetcarflexity.jpg\" width=\"600\" alt=\"Icon\"> </th>\n",
    "   </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the valid list of TTC Streetcar routes\n",
    "valid_routes = ['501','502','503','504','505','506','509','510','511','512','301','304','306','310']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['501',\n",
       " '502',\n",
       " '503',\n",
       " '504',\n",
       " '505',\n",
       " '506',\n",
       " '509',\n",
       " '510',\n",
       " '511',\n",
       " '512',\n",
       " '301',\n",
       " '304',\n",
       " '306',\n",
       " '310']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_directions = ['E/B','W/B','N/B','S/B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_days = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data\n",
    "- load pickled dataframe containing data loaded from source XLS files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in c:\\users\\ryanm\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (0.11.1)\n",
      "Requirement already satisfied: patsy>=0.5 in c:\\users\\ryanm\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from statsmodels) (0.5.1)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\ryanm\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from statsmodels) (1.4.1)\n",
      "Requirement already satisfied: pandas>=0.21 in c:\\users\\ryanm\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from statsmodels) (0.25.1)\n",
      "Requirement already satisfied: numpy>=1.14 in c:\\users\\ryanm\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from statsmodels) (1.17.1)\n",
      "Requirement already satisfied: six in c:\\users\\ryanm\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from patsy>=0.5->statsmodels) (1.12.0)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\ryanm\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pandas>=0.21->statsmodels) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\ryanm\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pandas>=0.21->statsmodels) (2019.2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using pip version 19.0.3, however version 20.2b1 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "! pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "# import statsmodels.api as sm\n",
    "import statsmodels as sm\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "# common imports\n",
    "import zipfile\n",
    "import time\n",
    "# import datetime, timedelta\n",
    "import datetime\n",
    "from datetime import datetime, timedelta\n",
    "from datetime import date\n",
    "from dateutil import relativedelta\n",
    "from io import StringIO\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.base import TransformerMixin\n",
    "from io import StringIO\n",
    "import requests\n",
    "import json\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import os\n",
    "import math\n",
    "from subprocess import check_output\n",
    "from IPython.display import display\n",
    "import logging\n",
    "import yaml\n",
    "from collections import Counter\n",
    "import re\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current directory is: C:\\personal\\manning\\deep_learning_for_structured_data\\notebooks\n",
      "path_to_yaml C:\\personal\\manning\\deep_learning_for_structured_data\\notebooks\\streetcar_data_preparation_config.yml\n"
     ]
    }
   ],
   "source": [
    "# load config file\n",
    "current_path = os.getcwd()\n",
    "print(\"current directory is: \"+current_path)\n",
    "\n",
    "path_to_yaml = os.path.join(current_path, 'streetcar_data_preparation_config.yml')\n",
    "print(\"path_to_yaml \"+path_to_yaml)\n",
    "try:\n",
    "    with open (path_to_yaml, 'r') as c_file:\n",
    "        config = yaml.safe_load(c_file)\n",
    "except Exception as e:\n",
    "    print('Error reading the config file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# common variables\n",
    "# control whether to load data from scratch from original source or from saved dataframe\n",
    "load_from_scratch = config['general']['load_from_scratch']\n",
    "# control whether to save dataframe with transformed data\n",
    "save_transformed_dataframe = config['general']['save_transformed_dataframe']\n",
    "# control whether rows containing erroneous values are removed from the saved dataset\n",
    "remove_bad_values = config['general']['remove_bad_values']\n",
    "# name of file containing pickled dataframe version of input (unprocessed) dataset\n",
    "pickled_input_dataframe = config['file_names']['pickled_input_dataframe']\n",
    "# name of file to which prepared data set is saved as a pickled dataframe\n",
    "pickled_output_dataframe = config['file_names']['pickled_output_dataframe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw path is C:\\personal\\manning\\deep_learning_for_structured_data\\notebooks\n",
      "path is C:\\personal\\manning\\deep_learning_for_structured_data\\data\n"
     ]
    }
   ],
   "source": [
    "# data is in a directory called \"data\" that is a sibling to the directory containing the notebook\n",
    "# this code assumes you have copied to this directory all the XLS files from the source dataset: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/#e8f359f0-2f47-3058-bf64-6ec488de52da\n",
    "rawpath = os.getcwd()\n",
    "print(\"raw path is\",rawpath)\n",
    "path = os.path.abspath(os.path.join(rawpath, '..', 'data'))\n",
    "print(\"path is\", path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_feature_categories(df):\n",
    "    allcols = list(df)\n",
    "    print(\"all cols\",allcols)\n",
    "    textcols = ['Incident','Location'] # \n",
    "    continuouscols = ['Min Delay','Min Gap'] \n",
    "                      # columns to deal with as continuous values - no embeddings\n",
    "    timecols = ['Report Date','Time']\n",
    "    collist = ['Day','Vehicle','Route','Direction']\n",
    "    for col in continuouscols:\n",
    "        df[col] = df[col].astype(float)\n",
    "    print('texcols: ',textcols)\n",
    "    print('continuouscols: ',continuouscols)\n",
    "    print('timecols: ',timecols)\n",
    "    print('collist: ',collist)\n",
    "    return(allcols,textcols,continuouscols,timecols,collist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_missing(dataset):\n",
    "    print(\"before mv\")\n",
    "    for col in collist:\n",
    "        dataset[col].fillna(value=\"missing\", inplace=True)\n",
    "    for col in continuouscols:\n",
    "        dataset[col].fillna(value=0.0,inplace=True)\n",
    "    for col in textcols:\n",
    "        dataset[col].fillna(value=\"missing\", inplace=True)\n",
    "    return (dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_time(date_time_value,time_value):\n",
    "    ''' given a datetime replace the time portion '''\n",
    "     \n",
    "    date_time_value = date_time_value.replace(hour=time_value.hour,minute=time_value.minute,second=time_value.minute)\n",
    "    return(date_time_value)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load dataframe and add columns for time series manipulation\n",
    "- load pickled dataframe\n",
    "- show info about the dataset\n",
    "- build index combining date+time along with labelled column date+time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pickled dataframe\n",
    "df = pd.read_pickle(os.path.join(path,pickled_input_dataframe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Delay</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Gap</th>\n",
       "      <th>Incident</th>\n",
       "      <th>Incident ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Min Gap</th>\n",
       "      <th>Report Date</th>\n",
       "      <th>Route</th>\n",
       "      <th>Time</th>\n",
       "      <th>Vehicle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Late Leaving Garage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dundas and Roncesvalles</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>505</td>\n",
       "      <td>06:31:00</td>\n",
       "      <td>4018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Utilized Off Route</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King and Shaw</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>12:43:00</td>\n",
       "      <td>4128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Held By</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kingston road and Bingham</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>501</td>\n",
       "      <td>14:01:00</td>\n",
       "      <td>4016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Investigation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King St. and Roncesvalles Ave.</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>14:22:00</td>\n",
       "      <td>4175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Utilized Off Route</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King and Bathurst</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>16:42:00</td>\n",
       "      <td>4080.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Day  Delay Direction  Gap             Incident  Incident ID  \\\n",
       "0  Thursday    NaN       E/B  NaN  Late Leaving Garage          NaN   \n",
       "1  Thursday    NaN       E/B  NaN   Utilized Off Route          NaN   \n",
       "2  Thursday    NaN       W/B  NaN              Held By          NaN   \n",
       "3  Thursday    NaN       W/B  NaN        Investigation          NaN   \n",
       "4  Thursday    NaN       E/B  NaN   Utilized Off Route          NaN   \n",
       "\n",
       "                         Location  Min Delay  Min Gap Report Date  Route  \\\n",
       "0         Dundas and Roncesvalles        4.0      8.0  2014-01-02    505   \n",
       "1                   King and Shaw       20.0     22.0  2014-01-02    504   \n",
       "2       Kingston road and Bingham       13.0     19.0  2014-01-02    501   \n",
       "3  King St. and Roncesvalles Ave.        7.0     11.0  2014-01-02    504   \n",
       "4               King and Bathurst        3.0      6.0  2014-01-02    504   \n",
       "\n",
       "       Time  Vehicle  \n",
       "0  06:31:00   4018.0  \n",
       "1  12:43:00   4128.0  \n",
       "2  14:01:00   4016.0  \n",
       "3  14:22:00   4175.0  \n",
       "4  16:42:00   4080.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ensure Route and Vehicle are strings, not numeric\n",
    "# df = df.astype({\"Route\": str, \"Vehicle\": int})\n",
    "df['Route'] = df['Route'].astype(str)\n",
    "df['Vehicle'] = df['Vehicle'].astype(str)\n",
    "# df['filename'] = df['filename'].str[:-4]\n",
    "df['Vehicle'] = df['Vehicle'].str[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all cols ['Day', 'Delay', 'Direction', 'Gap', 'Incident', 'Incident ID', 'Location', 'Min Delay', 'Min Gap', 'Report Date', 'Route', 'Time', 'Vehicle']\n",
      "texcols:  ['Incident', 'Location']\n",
      "continuouscols:  ['Min Delay', 'Min Gap']\n",
      "timecols:  ['Report Date', 'Time']\n",
      "collist:  ['Day', 'Vehicle', 'Route', 'Direction']\n"
     ]
    }
   ],
   "source": [
    "# define categories\n",
    "allcols,textcols,continuouscols,timecols,collist = define_feature_categories(df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before mv\n"
     ]
    }
   ],
   "source": [
    "# fill in missing values\n",
    "df = fill_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 78525 entries, 0 to 814\n",
      "Data columns (total 13 columns):\n",
      "Day            78525 non-null object\n",
      "Delay          3444 non-null float64\n",
      "Direction      78525 non-null object\n",
      "Gap            3434 non-null float64\n",
      "Incident       78525 non-null object\n",
      "Incident ID    889 non-null float64\n",
      "Location       78525 non-null object\n",
      "Min Delay      78525 non-null float64\n",
      "Min Gap        78525 non-null float64\n",
      "Report Date    78525 non-null datetime64[ns]\n",
      "Route          78525 non-null object\n",
      "Time           78525 non-null object\n",
      "Vehicle        78525 non-null object\n",
      "dtypes: datetime64[ns](1), float64(5), object(7)\n",
      "memory usage: 8.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# getting some information about dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78525, 13)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting some information about dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delay</th>\n",
       "      <th>Gap</th>\n",
       "      <th>Incident ID</th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Min Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>3444.000000</td>\n",
       "      <td>3434.000000</td>\n",
       "      <td>889.000000</td>\n",
       "      <td>78525.000000</td>\n",
       "      <td>78525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>14.503194</td>\n",
       "      <td>20.133663</td>\n",
       "      <td>3.901012</td>\n",
       "      <td>12.242840</td>\n",
       "      <td>17.477377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>38.477453</td>\n",
       "      <td>37.362669</td>\n",
       "      <td>2.861142</td>\n",
       "      <td>29.520808</td>\n",
       "      <td>33.127789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>996.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>4216.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Delay          Gap  Incident ID     Min Delay       Min Gap\n",
       "count  3444.000000  3434.000000   889.000000  78525.000000  78525.000000\n",
       "mean     14.503194    20.133663     3.901012     12.242840     17.477377\n",
       "std      38.477453    37.362669     2.861142     29.520808     33.127789\n",
       "min       0.000000     0.000000     1.000000      0.000000      0.000000\n",
       "25%       5.000000    10.000000     1.000000      4.000000      8.000000\n",
       "50%       7.000000    14.000000     5.000000      6.000000     12.000000\n",
       "75%      11.000000    20.000000     7.000000     11.000000     19.000000\n",
       "max     996.000000   999.000000    10.000000   1400.000000   4216.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# further Analysis \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day                    object\n",
       "Delay                 float64\n",
       "Direction              object\n",
       "Gap                   float64\n",
       "Incident               object\n",
       "Incident ID           float64\n",
       "Location               object\n",
       "Min Delay             float64\n",
       "Min Gap               float64\n",
       "Report Date    datetime64[ns]\n",
       "Route                  object\n",
       "Time                   object\n",
       "Vehicle                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Delay</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Gap</th>\n",
       "      <th>Incident</th>\n",
       "      <th>Incident ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Min Gap</th>\n",
       "      <th>Report Date</th>\n",
       "      <th>Route</th>\n",
       "      <th>Time</th>\n",
       "      <th>Vehicle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-02 06:31:31</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Late Leaving Garage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dundas and Roncesvalles</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>505</td>\n",
       "      <td>06:31:00</td>\n",
       "      <td>4018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 12:43:43</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Utilized Off Route</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King and Shaw</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>12:43:00</td>\n",
       "      <td>4128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 14:01:01</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Held By</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kingston road and Bingham</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>501</td>\n",
       "      <td>14:01:00</td>\n",
       "      <td>4016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 14:22:22</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Investigation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King St. and Roncesvalles Ave.</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>14:22:00</td>\n",
       "      <td>4175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 16:42:42</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Utilized Off Route</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King and Bathurst</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>16:42:00</td>\n",
       "      <td>4080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Day  Delay Direction  Gap             Incident  \\\n",
       "2014-01-02 06:31:31  Thursday    NaN       E/B  NaN  Late Leaving Garage   \n",
       "2014-01-02 12:43:43  Thursday    NaN       E/B  NaN   Utilized Off Route   \n",
       "2014-01-02 14:01:01  Thursday    NaN       W/B  NaN              Held By   \n",
       "2014-01-02 14:22:22  Thursday    NaN       W/B  NaN        Investigation   \n",
       "2014-01-02 16:42:42  Thursday    NaN       E/B  NaN   Utilized Off Route   \n",
       "\n",
       "                     Incident ID                        Location  Min Delay  \\\n",
       "2014-01-02 06:31:31          NaN         Dundas and Roncesvalles        4.0   \n",
       "2014-01-02 12:43:43          NaN                   King and Shaw       20.0   \n",
       "2014-01-02 14:01:01          NaN       Kingston road and Bingham       13.0   \n",
       "2014-01-02 14:22:22          NaN  King St. and Roncesvalles Ave.        7.0   \n",
       "2014-01-02 16:42:42          NaN               King and Bathurst        3.0   \n",
       "\n",
       "                     Min Gap Report Date Route      Time Vehicle  \n",
       "2014-01-02 06:31:31      8.0  2014-01-02   505  06:31:00    4018  \n",
       "2014-01-02 12:43:43     22.0  2014-01-02   504  12:43:00    4128  \n",
       "2014-01-02 14:01:01     19.0  2014-01-02   501  14:01:00    4016  \n",
       "2014-01-02 14:22:22     11.0  2014-01-02   504  14:22:00    4175  \n",
       "2014-01-02 16:42:42      6.0  2014-01-02   504  16:42:00    4080  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new column combing date + time (needed for resampling) and make it the index\n",
    "\n",
    "\n",
    "df.index = df.apply(lambda x: replace_time(x['Report Date'], x['Time']), axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create dataframes for delay-duration based forecasting\n",
    "- create a dataframe with just index and delay\n",
    "- resample to get mean by month in year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-02 06:31:31</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 12:43:43</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 14:01:01</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 14:22:22</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 16:42:42</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Min Delay\n",
       "2014-01-02 06:31:31        4.0\n",
       "2014-01-02 12:43:43       20.0\n",
       "2014-01-02 14:01:01       13.0\n",
       "2014-01-02 14:22:22        7.0\n",
       "2014-01-02 16:42:42        3.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new = old[['A', 'C', 'D']].copy()\n",
    "# new dataframe with just Min Delay column\n",
    "dfstrip = df[['Min Delay']].copy()\n",
    "dfstrip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>7.862069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>14.117978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>12.580595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>12.624365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>12.280083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Min Delay\n",
       "2014-01-31   7.862069\n",
       "2014-02-28  14.117978\n",
       "2014-03-31  12.580595\n",
       "2014-04-30  12.624365\n",
       "2014-05-31  12.280083"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmonthav = dfstrip.resample('M').mean()\n",
    "dfmonthav.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df3['new'] = df3.index\n",
    "dfmonthav['Report Date Time'] = dfmonthav.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Report Date Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>13.590000</td>\n",
       "      <td>2019-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>16.057428</td>\n",
       "      <td>2019-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>13.194384</td>\n",
       "      <td>2019-10-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2019-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Min Delay Report Date Time\n",
       "2019-08-31  13.590000       2019-08-31\n",
       "2019-09-30  16.057428       2019-09-30\n",
       "2019-10-31  13.194384       2019-10-31\n",
       "2019-11-30   0.000000       2019-11-30\n",
       "2019-12-31   0.000000       2019-12-31"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmonthav.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create dataframe for delay count based forecasting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Delay</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Gap</th>\n",
       "      <th>Incident</th>\n",
       "      <th>Incident ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Min Gap</th>\n",
       "      <th>Report Date</th>\n",
       "      <th>Route</th>\n",
       "      <th>Time</th>\n",
       "      <th>Vehicle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-02 06:31:31</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Late Leaving Garage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dundas and Roncesvalles</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>505</td>\n",
       "      <td>06:31:00</td>\n",
       "      <td>4018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 12:43:43</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Utilized Off Route</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King and Shaw</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>12:43:00</td>\n",
       "      <td>4128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 14:01:01</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Held By</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kingston road and Bingham</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>501</td>\n",
       "      <td>14:01:00</td>\n",
       "      <td>4016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 14:22:22</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Investigation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King St. and Roncesvalles Ave.</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>14:22:00</td>\n",
       "      <td>4175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 16:42:42</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Utilized Off Route</td>\n",
       "      <td>NaN</td>\n",
       "      <td>King and Bathurst</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>16:42:00</td>\n",
       "      <td>4080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Day  Delay Direction  Gap             Incident  \\\n",
       "2014-01-02 06:31:31  Thursday    NaN       E/B  NaN  Late Leaving Garage   \n",
       "2014-01-02 12:43:43  Thursday    NaN       E/B  NaN   Utilized Off Route   \n",
       "2014-01-02 14:01:01  Thursday    NaN       W/B  NaN              Held By   \n",
       "2014-01-02 14:22:22  Thursday    NaN       W/B  NaN        Investigation   \n",
       "2014-01-02 16:42:42  Thursday    NaN       E/B  NaN   Utilized Off Route   \n",
       "\n",
       "                     Incident ID                        Location  Min Delay  \\\n",
       "2014-01-02 06:31:31          NaN         Dundas and Roncesvalles        4.0   \n",
       "2014-01-02 12:43:43          NaN                   King and Shaw       20.0   \n",
       "2014-01-02 14:01:01          NaN       Kingston road and Bingham       13.0   \n",
       "2014-01-02 14:22:22          NaN  King St. and Roncesvalles Ave.        7.0   \n",
       "2014-01-02 16:42:42          NaN               King and Bathurst        3.0   \n",
       "\n",
       "                     Min Gap Report Date Route      Time Vehicle  \n",
       "2014-01-02 06:31:31      8.0  2014-01-02   505  06:31:00    4018  \n",
       "2014-01-02 12:43:43     22.0  2014-01-02   504  12:43:00    4128  \n",
       "2014-01-02 14:01:01     19.0  2014-01-02   501  14:01:00    4016  \n",
       "2014-01-02 14:22:22     11.0  2014-01-02   504  14:22:00    4175  \n",
       "2014-01-02 16:42:42      6.0  2014-01-02   504  16:42:00    4080  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-02 06:31:31</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 12:43:43</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 14:01:01</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 14:22:22</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-02 16:42:42</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Min Delay\n",
       "2014-01-02 06:31:31        4.0\n",
       "2014-01-02 12:43:43       20.0\n",
       "2014-01-02 14:01:01       13.0\n",
       "2014-01-02 14:22:22        7.0\n",
       "2014-01-02 16:42:42        3.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfstrip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delay Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>1278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Delay Count\n",
       "2014-01-31          319\n",
       "2014-02-28          178\n",
       "2014-03-31         1278\n",
       "2014-04-30         1182\n",
       "2014-05-31          964"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmonthcount = dfstrip.resample('M').count()\n",
    "# df = df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'})\n",
    "dfmonthcount = dfmonthcount.rename(columns={'Min Delay':'Delay Count'})\n",
    "dfmonthcount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delay Count</th>\n",
       "      <th>Report Date Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>319</td>\n",
       "      <td>2014-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>178</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>1278</td>\n",
       "      <td>2014-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>1182</td>\n",
       "      <td>2014-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>964</td>\n",
       "      <td>2014-05-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Delay Count Report Date Time\n",
       "2014-01-31          319       2014-01-31\n",
       "2014-02-28          178       2014-02-28\n",
       "2014-03-31         1278       2014-03-31\n",
       "2014-04-30         1182       2014-04-30\n",
       "2014-05-31          964       2014-05-31"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmonthcount['Report Date Time'] = dfmonthcount.index\n",
    "dfmonthcount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Report Date Time_left</th>\n",
       "      <th>Delay Count</th>\n",
       "      <th>Report Date Time_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>7.862069</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>319</td>\n",
       "      <td>2014-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>14.117978</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>178</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>12.580595</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>1278</td>\n",
       "      <td>2014-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>12.624365</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>1182</td>\n",
       "      <td>2014-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>12.280083</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>964</td>\n",
       "      <td>2014-05-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Min Delay Report Date Time_left  Delay Count  \\\n",
       "2014-01-31   7.862069            2014-01-31          319   \n",
       "2014-02-28  14.117978            2014-02-28          178   \n",
       "2014-03-31  12.580595            2014-03-31         1278   \n",
       "2014-04-30  12.624365            2014-04-30         1182   \n",
       "2014-05-31  12.280083            2014-05-31          964   \n",
       "\n",
       "           Report Date Time_right  \n",
       "2014-01-31             2014-01-31  \n",
       "2014-02-28             2014-02-28  \n",
       "2014-03-31             2014-03-31  \n",
       "2014-04-30             2014-04-30  \n",
       "2014-05-31             2014-05-31  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merged_data = merged_data.join(dm_cases.set_index('CASE_NUMBER'), on = 'CASE_NUMBER')\n",
    "dfmonthav = dfmonthav.join(dfmonthcount, on='Report Date Time',lsuffix='_left', rsuffix='_right')\n",
    "dfmonthav.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Report Date Time_left</th>\n",
       "      <th>Delay Count</th>\n",
       "      <th>Report Date Time_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>7.862069</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>319</td>\n",
       "      <td>2014-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>14.117978</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>178</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>12.580595</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>1278</td>\n",
       "      <td>2014-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>12.624365</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>1182</td>\n",
       "      <td>2014-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>12.280083</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>964</td>\n",
       "      <td>2014-05-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Min Delay Report Date Time_left  Delay Count  \\\n",
       "2014-01-31   7.862069            2014-01-31          319   \n",
       "2014-02-28  14.117978            2014-02-28          178   \n",
       "2014-03-31  12.580595            2014-03-31         1278   \n",
       "2014-04-30  12.624365            2014-04-30         1182   \n",
       "2014-05-31  12.280083            2014-05-31          964   \n",
       "\n",
       "           Report Date Time_right  \n",
       "2014-01-31             2014-01-31  \n",
       "2014-02-28             2014-02-28  \n",
       "2014-03-31             2014-03-31  \n",
       "2014-04-30             2014-04-30  \n",
       "2014-05-31             2014-05-31  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmonthav.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Report Date Time</th>\n",
       "      <th>Delay Count</th>\n",
       "      <th>Report Date Time_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>7.862069</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>319</td>\n",
       "      <td>2014-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>14.117978</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>178</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>12.580595</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>1278</td>\n",
       "      <td>2014-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>12.624365</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>1182</td>\n",
       "      <td>2014-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>12.280083</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>964</td>\n",
       "      <td>2014-05-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Min Delay Report Date Time  Delay Count Report Date Time_right\n",
       "2014-01-31   7.862069       2014-01-31          319             2014-01-31\n",
       "2014-02-28  14.117978       2014-02-28          178             2014-02-28\n",
       "2014-03-31  12.580595       2014-03-31         1278             2014-03-31\n",
       "2014-04-30  12.624365       2014-04-30         1182             2014-04-30\n",
       "2014-05-31  12.280083       2014-05-31          964             2014-05-31"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmonthav = dfmonthav.rename(columns={'Report Date Time_left':'Report Date Time'})\n",
    "dfmonthav.drop(['Report Date Time_right'],axis=1)\n",
    "dfmonthav.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Report Date Time</th>\n",
       "      <th>Delay Count</th>\n",
       "      <th>Report Date Time_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>7.862069</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>319</td>\n",
       "      <td>2014-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>14.117978</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>178</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>12.580595</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>1278</td>\n",
       "      <td>2014-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>12.624365</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>1182</td>\n",
       "      <td>2014-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>12.280083</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>964</td>\n",
       "      <td>2014-05-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Min Delay Report Date Time  Delay Count Report Date Time_right\n",
       "2014-01-31   7.862069       2014-01-31          319             2014-01-31\n",
       "2014-02-28  14.117978       2014-02-28          178             2014-02-28\n",
       "2014-03-31  12.580595       2014-03-31         1278             2014-03-31\n",
       "2014-04-30  12.624365       2014-04-30         1182             2014-04-30\n",
       "2014-05-31  12.280083       2014-05-31          964             2014-05-31"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmonthav.drop(['Report Date Time_right'],axis=1)\n",
    "dfmonthav.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2bc25da08>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfmonthav['Min Delay'].plot(legend=True)\n",
    "# df.B.plot(secondary_y=True, style='g')\n",
    "dfmonthav['Delay Count'].plot(secondary_y=True, style='g',legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#ax.set_ylabel('Average Delay')\n",
    "#ax.right_ax.set_ylabel('Number of delays')\n",
    "plt.rcParams[\"figure.figsize\"] = [8,4]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Forecasting\n",
    "**Trend** - Trend shows the variation in the output as time increases, often in a non-linear fashion. \n",
    "\n",
    "**Level** - The baseline value for a time series.\n",
    "\n",
    "**Seasonality** - is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year. Any predictable fluctuation or pattern that recurs or repeats over a one-year period is said to be seasonal - from https://www.investopedia.com/terms/s/seasonality.asp\n",
    "\n",
    "**Noise** - Random variation in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 72 entries, 2014-01-31 to 2019-12-31\n",
      "Freq: M\n",
      "Data columns (total 4 columns):\n",
      "Min Delay                 72 non-null float64\n",
      "Report Date Time          72 non-null datetime64[ns]\n",
      "Delay Count               72 non-null int64\n",
      "Report Date Time_right    72 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(1), int64(1)\n",
      "memory usage: 2.8 KB\n"
     ]
    }
   ],
   "source": [
    "dfmonthav.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Report Date Time</th>\n",
       "      <th>Delay Count</th>\n",
       "      <th>Report Date Time_right</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>7.862069</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>319</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>14.117978</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>178</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>12.580595</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>1278</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>12.624365</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>1182</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>12.280083</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>964</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>15.066437</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>1159</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>13.701789</td>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>1006</td>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>12.505335</td>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>1031</td>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>13.930567</td>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>1181</td>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>16.803625</td>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>993</td>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>15.501160</td>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>862</td>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>13.972540</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>874</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>11.913779</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>1241</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>11.418465</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>1668</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>13.610687</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>1048</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>13.791139</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>948</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>16.212888</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>869</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>16.766834</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>995</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>11.854423</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>893</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>12.429012</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>972</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Min Delay Report Date Time  Delay Count Report Date Time_right  \\\n",
       "2014-01-31   7.862069       2014-01-31          319             2014-01-31   \n",
       "2014-02-28  14.117978       2014-02-28          178             2014-02-28   \n",
       "2014-03-31  12.580595       2014-03-31         1278             2014-03-31   \n",
       "2014-04-30  12.624365       2014-04-30         1182             2014-04-30   \n",
       "2014-05-31  12.280083       2014-05-31          964             2014-05-31   \n",
       "2014-06-30  15.066437       2014-06-30         1159             2014-06-30   \n",
       "2014-07-31  13.701789       2014-07-31         1006             2014-07-31   \n",
       "2014-08-31  12.505335       2014-08-31         1031             2014-08-31   \n",
       "2014-09-30  13.930567       2014-09-30         1181             2014-09-30   \n",
       "2014-10-31  16.803625       2014-10-31          993             2014-10-31   \n",
       "2014-11-30  15.501160       2014-11-30          862             2014-11-30   \n",
       "2014-12-31  13.972540       2014-12-31          874             2014-12-31   \n",
       "2015-01-31  11.913779       2015-01-31         1241             2015-01-31   \n",
       "2015-02-28  11.418465       2015-02-28         1668             2015-02-28   \n",
       "2015-03-31  13.610687       2015-03-31         1048             2015-03-31   \n",
       "2015-04-30  13.791139       2015-04-30          948             2015-04-30   \n",
       "2015-05-31  16.212888       2015-05-31          869             2015-05-31   \n",
       "2015-06-30  16.766834       2015-06-30          995             2015-06-30   \n",
       "2015-07-31  11.854423       2015-07-31          893             2015-07-31   \n",
       "2015-08-31  12.429012       2015-08-31          972             2015-08-31   \n",
       "\n",
       "            Month  \n",
       "2014-01-31      1  \n",
       "2014-02-28      2  \n",
       "2014-03-31      3  \n",
       "2014-04-30      4  \n",
       "2014-05-31      5  \n",
       "2014-06-30      6  \n",
       "2014-07-31      7  \n",
       "2014-08-31      8  \n",
       "2014-09-30      9  \n",
       "2014-10-31     10  \n",
       "2014-11-30     11  \n",
       "2014-12-31     12  \n",
       "2015-01-31      1  \n",
       "2015-02-28      2  \n",
       "2015-03-31      3  \n",
       "2015-04-30      4  \n",
       "2015-05-31      5  \n",
       "2015-06-30      6  \n",
       "2015-07-31      7  \n",
       "2015-08-31      8  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a column to make scatter plot by month convenient\n",
    "dfmonthav['Month'] = dfmonthav['Report Date Time'].dt.month\n",
    "dfmonthav.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min Delay</th>\n",
       "      <th>Report Date Time</th>\n",
       "      <th>Delay Count</th>\n",
       "      <th>Report Date Time_right</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>7.862069</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>319</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>14.117978</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>178</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>12.580595</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>1278</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>12.624365</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>1182</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>12.280083</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>964</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>15.066437</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>1159</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>13.701789</td>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>1006</td>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>12.505335</td>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>1031</td>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>13.930567</td>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>1181</td>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>16.803625</td>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>993</td>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>15.501160</td>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>862</td>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>13.972540</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>874</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>11.913779</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>1241</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>11.418465</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>1668</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>13.610687</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>1048</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>13.791139</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>948</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>16.212888</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>869</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>16.766834</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>995</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>11.854423</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>893</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>12.429012</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>972</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Min Delay Report Date Time  Delay Count Report Date Time_right  \\\n",
       "2014-01-31   7.862069       2014-01-31          319             2014-01-31   \n",
       "2014-02-28  14.117978       2014-02-28          178             2014-02-28   \n",
       "2014-03-31  12.580595       2014-03-31         1278             2014-03-31   \n",
       "2014-04-30  12.624365       2014-04-30         1182             2014-04-30   \n",
       "2014-05-31  12.280083       2014-05-31          964             2014-05-31   \n",
       "2014-06-30  15.066437       2014-06-30         1159             2014-06-30   \n",
       "2014-07-31  13.701789       2014-07-31         1006             2014-07-31   \n",
       "2014-08-31  12.505335       2014-08-31         1031             2014-08-31   \n",
       "2014-09-30  13.930567       2014-09-30         1181             2014-09-30   \n",
       "2014-10-31  16.803625       2014-10-31          993             2014-10-31   \n",
       "2014-11-30  15.501160       2014-11-30          862             2014-11-30   \n",
       "2014-12-31  13.972540       2014-12-31          874             2014-12-31   \n",
       "2015-01-31  11.913779       2015-01-31         1241             2015-01-31   \n",
       "2015-02-28  11.418465       2015-02-28         1668             2015-02-28   \n",
       "2015-03-31  13.610687       2015-03-31         1048             2015-03-31   \n",
       "2015-04-30  13.791139       2015-04-30          948             2015-04-30   \n",
       "2015-05-31  16.212888       2015-05-31          869             2015-05-31   \n",
       "2015-06-30  16.766834       2015-06-30          995             2015-06-30   \n",
       "2015-07-31  11.854423       2015-07-31          893             2015-07-31   \n",
       "2015-08-31  12.429012       2015-08-31          972             2015-08-31   \n",
       "\n",
       "            Month  \n",
       "2014-01-31      1  \n",
       "2014-02-28      2  \n",
       "2014-03-31      3  \n",
       "2014-04-30      4  \n",
       "2014-05-31      5  \n",
       "2014-06-30      6  \n",
       "2014-07-31      7  \n",
       "2014-08-31      8  \n",
       "2014-09-30      9  \n",
       "2014-10-31     10  \n",
       "2014-11-30     11  \n",
       "2014-12-31     12  \n",
       "2015-01-31      1  \n",
       "2015-02-28      2  \n",
       "2015-03-31      3  \n",
       "2015-04-30      4  \n",
       "2015-05-31      5  \n",
       "2015-06-30      6  \n",
       "2015-07-31      7  \n",
       "2015-08-31      8  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmonthav.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot to show mean delay in same month across years\n",
    "dfmonthav.plot.scatter(x = 'Month', y = 'Min Delay')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot to show delay count in same month across years\n",
    "dfmonthav.plot.scatter(x = 'Month', y = 'Delay Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trend Analysis\n",
    "\n",
    "Use the built-in Pandas rolling function to smooth the data.\n",
    "\n",
    "For trend analysis, we use smoothing techniques. \n",
    "- In statistics smoothing a data set means to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. \n",
    "- In smoothing, the data points of a signal are modified so individual points (presumably because of noise) are reduced, and points that are lower than the adjacent points are increased leading to a smoother signal. \n",
    "\n",
    "We implement smoothing by taking moving averages. [Exponential moving average](https://www.investopedia.com/terms/e/ema.asp) is frequently used to compute smoothed function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_delay = dfmonthav[['Min Delay']]\n",
    "count_delay = dfmonthav[['Delay Count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_delay.rolling(6).mean().plot(figsize=(20,10), linewidth=5, fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_delay.rolling(6).mean().plot(figsize=(20,10), linewidth=5, fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seasonality Analysis\n",
    "\n",
    "Two examples of seasonality analysis algorithms are:\n",
    "\n",
    "[Using 1st discrete difference of object](https://machinelearningmastery.com/difference-time-series-dataset-python/)\n",
    "\n",
    "[Periodicity and Autocorrelation](https://machinelearningmastery.com/gentle-introduction-autocorrelation-partial-autocorrelation/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using 1st discrete difference of object\n",
    "# This gives us idea about variation of data for a period of time.\n",
    "mean_delay.diff(periods=4).plot(figsize=(20,10), linewidth=5, fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_delay.diff(periods=4).plot(figsize=(20,10), linewidth=5, fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auto correlation is the most famous way to understand seasonal variation till now. We can calculate the correlation for time series observations with observations with previous time steps, called lags. Because the correlation of the time series observations is calculated with values of the same series at previous times, this is called a serial correlation, or an autocorrelation.In this plot vertical axis is represented by the following equations:-\n",
    "\n",
    "> $C_n = \\sum_{t = 1}^{n - h} (y(t) - \\hat{y}) (y(t + n) - \\hat{y}) / n$\n",
    "\n",
    "> $C_0 = \\sum_{t = 1}^{n} (y(t) - \\hat{y})^2 / n$\n",
    "\n",
    "Horizontal axis represents time lag(previous time steps)  h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mean_delay = dfmonthav[['Min Delay']]\n",
    "# count_delay = dfmonthav[['Delay Count']]\n",
    "\n",
    "\n",
    "pd.plotting.autocorrelation_plot(dfmonthav['Min Delay'])\n",
    "plt.show()\n",
    "# The following curve represents the relation between current time step and its previous time step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.autocorrelation_plot(dfmonthav['Delay Count'])\n",
    "plt.show()\n",
    "# The following curve represents the relation between current time step and its previous time step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.lag_plot(dfmonthav['Min Delay'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.lag_plot(dfmonthav['Delay Count'])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
